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Performance of different augmented writing tools on german job ads
<p>This dataset was created as part of an empirical analysis of four German-language augmented writing technologies for detecting gender exclusion. For this purpose, approximately 160,000 job postings from three different platforms were collected and evaluated using the technologies. The dataset primarily contains the number of expressions extracted per job posting, as well as the gender scores and categories calculated by the technologies. Together with variable descriptions and the list of keywords used to sample the leading positions, this dataset serves as additional information for a manuscript under review.</p>
German Compounds and Paraphrases
<p>++++ english below ++++</p> <p>Beschreibung der enthaltenen Datensätze:</p> <p><br> all-phrases.csv:</p> <p>Was beinhalten die Spalten?</p> <p>| Kompositum | Erstes Kompositionsglied | Zweites Kompositionsglied | Frequenz des Kompositums im Korpus* | Paraphrasierung | Frequenz der Paraphrasierung im Korpus* |</p> <p>*Der Korpus, welcher für Extraktion der Phrasen und beider Frequenzwerte verwendet wurde, zu finden unter: https://corpora.uni-leipzig.de/de?corpusId=deu_newscrawl_2011</p> <p><br> labeled-phrases.csv:</p> <p>Hat den gleichen Aufbau wie all-phrases.csv mit einer zusatzlichen Spalte:</p> <p>|...| Label |</p> <p>Das Label hat die Werte 0 oder 1. Es ist eine Bewertung der zugehörigen Paraphrasierung, wobei 1 für 'richtig' und 1 für 'falsch' steht.</p> <p> </p> <p>++++ english ++++</p> <p>discription of datasets:</p> <p><br> all-phrases.csv:</p> <p>What do the columns contain?</p> <p>| compound word | first element of compound | second element of compound | frequency of compound in corpus* | phrase | frequency of phrase in corpus* |</p> <p>*Meaning the corpus used to obtain the phrases and both frequencies. Can be found here: https://corpora.uni-leipzig.de/de?corpusId=deu_newscrawl_2011</p> <p><br> labeled-phrases.csv:</p> <p>this one has the same columns as the previous dataset with one addition:</p> <p>|...| label |</p> <p>the labes has values of either 0 or 1. It is an evaluation of the correctness of the phrase with 1 meaning 'correct' and 0 meaning 'false'. </p>
Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150)
<p>Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150). Details are reported in: Kriston L, Schäfer J, Jacob GA, Härter M, Hölzel LP. Reliability and validity of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3). <em>Eur J Psychol Assess</em> 2013; 29: 205-212.</p> <p>IMPORTANT: This is an opportunity sample that is not representative of any well-defined population. Accordingly, the values should not be used as reference or norm values for the German version of the YSQ-S3.</p>
dataset NWRT (nonword repetition task) Italian-German
<p>The dataset contains raw and aggregated data with scores obtained by participant children (Italian-German bilingual children) subdivided in three groups (typically developing, at-risk, diagnosed with developmental language disorder). as well as in the two groups presence/absence of phonological risk, on a new Nonword Repetition Task. Rows in the file correspond to single nonwords, belonging to one of the subsets: LS (language-specific), LNS (llanguage-non-specific), for each language (IT, Italian, GER, German).</p> <p>The dataset has been the basis for the analyses reported in the paper "A Nonword Repetition Task Discriminates Typically Developing Italian-German Bilingual Children From Bilingual Children With Developmental Language Disorder: The Role of Language-Specific and Language-Non-specific Nonwords", part of the research topic "Discriminating the Linguistic Profiles of Bilingual Children and Children with Language Impairment", Frontiers in Psychology, 2022</p>
Pennsylvania German word list (lemmatized and POS-annotated)
<p>The file presents the words used in the Pennsylvania German part of the ENDE corpus (www.deitsch.eu). The list contains every lemma with its associated word forms documented in the corpus, comprised of 1761 lemmata and 2704 word forms.</p> <p>The ENDE corpus (“English-Deitsch translation corpus”) is the first POS-annotated and searchable text corpus in Pennsylvania German (= Deitsch; ISO language code: pdc), aligned to the English source texts. Despite many digital texts in Deitsch are available on the internet, there are, so far, no digital corpora for this language. This is due mainly to the lack of a generally recognized standard variety which could serve as a reference point for the linguistic analysis needed for lemmatization and annotation.</p> <p>Lemmatization was done with the help of different lexicographic resources (https://www.deitsch.eu/news/view/9) most of which follow other spelling conventions. A fair number of word forms, especially English loanwords of some sort, cannot be found in the dictionaries. Moreover, the variety used here is characterized by a high variability regarding not only the spelling but also other aspects of the language.</p> <p>Part-of-speech tags were assigned manually (see tagsets A and B below). These tagsets for part-of-speech annotation of Deitsch texts are based on the 2017 version of the STTS system created and widely used for German (https://ids-pub.bsz-bw.de/frontdoor/deliver/index/docId/6063/file/Westpfahl_Schmidt_Jonietz_Borlinghaus_STTS_2_0_2017.pdf), which has been slightly modified and adapted to the corpus texts written in the Plain Deitsch variety. Tagset A gives a broader view and refers to the lemma level, tagset B is more fine-grained and suitable for the single word forms documented in the corpus. Only those tags are listed which are actually employed for the annotation of the corpus texts. Foreign items not integrated in the Deitsch text flow (e.g. English quotations) have been omitted.</p> <p>For more details about the corpus and the project please refer to the above mentioned website.</p>
Polifonia Corpus - Encyclopedic Module Metadata - German Language
<p>We make available the Metadata related to the Wikipedia pages that constitute the Encyclopedic Module of the Polifonia Textual Corpus. Metadata for this module includes, per each Wikipedia page, its Wikipedia ID, BabelNet ID, gloss, resource type (that can be named entity or concept), Lemmata, Sensekey, WikiData ID.</p> <p>Full description at <a href="http://Full description at https://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
Secondary Data: Measuring Person-centred Care in German Nursing Homes – Exploring Construct Validity of the Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis
<p>This is the secondary data set and R-Code of R statistical software (version 4.0.4) to explore construct validity of the German Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis.</p>
The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities
<p>This dataset contains the final TEI/XML version of the two-volume printed work “<a href="https://nbn-resolving.de/urn:nbn:de:hebis:77-vcol-20056">The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities</a>” which was produced during the Second World War by the British Foreign Office and the Ministry of Economic Warfare.</p> <p>This file has been created by the DH Lab at the <a href="https://www.ieg-mainz.de/likecms.php?function=set_lang&lang=en">Leibniz Institute of European History</a> (Mainz) and <a href="https://textloop.de/">textloop</a>.</p> <p><a href="https://doi.org/10.5281/zenodo.6370214">Previous XML versions (without TEI).</a></p> <p> </p> <p> </p>
Data and code from: Functional rarity of plants in German hay meadows - patterns on the species level and mismatches with community species richness
<p>Functional rarity (FR) - a feature combining a species' rarity with the distinctiveness of its traits - represents a promising tool to better understand the ecological importance of rare species and consequently to protect functional diversity more efficiently. Yet, we lack a systematic understanding of FR on both the species level (which species are functionally rare and why) and the community level (how is FR associated with biodiversity and environmental conditions). Here, we quantify FR for 218 plant species from German hay meadows on a local, regional, and national scale by combining data from 6500 vegetation relevés and 15 ecologically relevant traits. We investigate the association between rarity and trait distinctiveness on different spatial scales via correlation measures and show which traits lead to low or high trait distinctiveness via distance-based redundancy analysis. We test how species richness and FR are correlated and use boosted regression trees to determine environmental conditions driving species richness and FR. On the local scale, only rare species showed high trait distinctiveness while on larger spatial scales rare and common species showed high trait distinctiveness. As infrequent trait attributes (e.g., legumes, low clonality) led to higher trait distinctiveness, we argue that functionally rare species are either specialists or transients. While specialists occupy a particular niche in hay meadows leading to lower rarity on larger spatial scales, transients display distinct but maladaptive traits resulting in high rarity across all spatial scales. More functionally rare species than expected by chance occurred in species-poor communities indicating that they prefer environmental conditions differing from characteristic conditions of species-rich hay meadows. Finally, we argue that functionally rare species are not necessarily relevant for nature conservation, since many were transients from surrounding habitats. Yet, FR can facilitate our understanding of why species are rare in a habitat and under which conditions these species occur.</p>
Surgical case mixes and distributions of perioperative surgical process durations for German hospitals
<p>The data set consists of parameters of distributions of perioperative surgical process durations in German hospitals subdivided by the level of care, the surgical specialty, patient type (in- or outpatient), and the procedure’s main OPS code. We consider the following processes: anesthesia induction time, anesthesia emergence time, surgical lead-in, incision-to-closure time, surgical lead-out, and closure-to-incision time (as described in the German Perioperative Procedural Time Glossary). In addition, we provide the number of cases (classified by the procedure’s main OPS code) treated in one year per level of care, surgical specialty, and patient type (in- or outpatient).</p> <p>The supplied data set is the result of processing the 2019 surgical process data set from the Operating Room benchmarking program of German-speaking countries provided by the company digmed GmbH. In total, we considered 2,035,126 recorded surgeries from 212 different hospitals. The data set was created and published to facilitate and promote research on Operating Room planning in the field of Operations Research. It can be used for generating specific problem instances or benchmark sets for Operating Room planning problems.</p> <p> </p> <p> </p>
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.
Aerial and Terrestrial Thermal Images of German Multi-Family Buildings
<p>This dataset consists of 968 thermal images including 693 captured by hand-held camera on the ground and 275 via UAV. Of the aerial images, 139 were recorded manually and 136 in automatic flight mode. The images depict four multi-family buildings of 18 m height in the German city of Karlsruhe belonging to the local municipal housing association Volkswohnung Karlsruhe GmbH. Table 1 gives an overview of the buildings in question, all of which were fully rented out on the days of image acquisition.</p> <p><strong>Table 1:</strong> Building information</p> <table> <tbody> <tr> <td> <p><sup> Building</sup><br> <sub>Details</sub></p> </td> <td> <p>Sophienstr. 201-203</p> </td> <td> <p>Volzstr. 2</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Wichernstr. 10-18</p> </td> </tr> <tr> <td> <p>construction year</p> </td> <td> <p>1957</p> </td> <td> <p>1954</p> </td> <td> <p>1953</p> </td> <td> <p>1953</p> </td> </tr> <tr> <td> <p>apartments</p> </td> <td> <p>30</p> </td> <td> <p>12</p> </td> <td> <p>25</p> </td> <td> <p>30</p> </td> </tr> </tbody> </table> <p>The aerial images were acquired using DJI’s “Matrice 600” UAV (DJI, 2022) equipped with the “Zenmuse XT2”, a combination of FLIR’s “Duo Pro R” thermal and RGB camera technology and DJI’s gimbal (FLIR, 2021a). All thermal images were recorded in FLIR’s proprietary image format RJPEG. The terrestrial thermographic images were captured with FLIR’s “T200” hand-held camera (FLIR, 2021b) in the standard JPEG format. The emissivity was set to 0.95 throughout the acquisition of both aerial and terrestrial images. Thermographic image processing and analysis was realized using the "FLIR Thermal Studio" software (FLIR Systems Inc., 2022). The temperature scale was set to -8 °C to +13 °C and color distribution function "signal linear" selected.</p> <p>The thermal images of this dataset were recorded on February 28<sup>th</sup> and March 1<sup>st</sup>, 2022, between 8 p.m. and 1 a.m. On February 28<sup>th</sup> the outside air temperature registered at between 1 °C and 3 °C. Wind speeds reached a maximum of 17 km/h. The sky was cloudless both during the flights and in the preceding 24 hours. A maximum temperature of 11 °C was recorded by local weather stations in that time period. Very similar weather conditions were present on March 1<sup>st</sup>. The outside air temperature was recorded at between -1 °C and 5 °C during acquisition, with wind speeds of max. 11 km/h. Again, the sky was entirely clear both during the flights and in the preceding 24 hours, with a maximum temperature of 9 °C present in that time period. The sun set at around 6:10 p.m. on both days (timeanddate, 2022).</p> <p>The images were recorded using ten different flight settings of varying speed, flight height, and camera angle. Details are summarized in Table 2.</p> <p><strong>Table 2: </strong>Flight settings</p> <table align="center"> <tbody> <tr> <td> <p>Flight</p> </td> <td> <p>Building</p> </td> <td> <p>Automatically/ manually performed flight route</p> </td> <td> <p>Camera angle</p> <p>[°]</p> </td> <td> <p>Height above ground</p> <p>[m]</p> </td> <td> <p>Height above building</p> <p>[m]</p> </td> <td> <p>Distance to façade</p> <p>[m]</p> </td> <td> <p>Flight speed</p> <p>[m/s]</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>60</p> </td> <td> <p>42</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>90 (nadir)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>90 (nadir)</p> </td> <td> <p>60</p> </td> <td> <p>42</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>4</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>15</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Wichernstr. 10-18</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>15</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgments:</strong> The authors appreciate the support of Marinus Vogl (Air Bavarian GmbH) in acquiring the thermal images via UAV. Moreover, they thank Harald Schneider (Karlsruhe Institute of Technology) for his advice and assistance. Lastly, they gratefully acknowledge the consent and support of Karlsruher Volkswohnung GmbH within this research project.</p> <p> </p> <p><strong>References:</strong></p> <p>DJI (2022). Matrice 600 - DJI. URL: https://www.dji.com/de/matrice600 (accessed 10<sup>th</sup> January 2022)</p> <p>FLIR (2021a). FLIR XT2 product information (Wilsonville, USA). URL: https://www.flir.de/products/xt2/ (accessed 10<sup>th</sup> January 2022)</p> <p>FLIR (2021b). FLIR T-series (Wilsonville, USA). URL: https://www.flir.com/instruments/t-series/ (accessed 10<sup>th</sup> January 2022)</p> <p>FLIR Systems Inc. (2022). User’s manual Flir Thermal Studio. URL: https://www.sahkonumerot.fi/6708162/doc/operatinginstructions/ (accessed 12<sup>th</sup> August 2022)</p> <p>Timeanddate (2022). Wetter im Februar 2022 in Karlsruhe, Baden-Württemberg, Deutschland. URL: https://www.timeanddate.de/wetter/deutschland/karlsruhe/rueckblick?month=2&year=2022 (accessed 12<sup>th</sup> March 2022)</p> <p> </p>
The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag
<p>The German Bundestag elections are the most important democratic elections of Germany. This dataset comprises Twitter interactions related with German politicians of the most important political parties over several months in the (pre-)phase of the German election campaigns in 2017. The Twitter accounts of 364 politicians (that is approximately half of the German parliament, the German Bundestag) were followed for almost half a year. The collected data comprise of about 10 GB of Twitter raw data generated by more than 120.000 active Twitter users generating more than 1.200.000 tweets during the pre- and hot-phase of the election campaigns for the 19th German Bundestag. <br> The dataset can be used to study how political parties, their followers and supporters make use of social media channels like Twitter in the context of political election campaigns and what kind of content is shared.</p> <p>The following files contain relevant context information:</p> <ul> <li><strong>crawled-pages.json</strong> contains the URLs of the official party faction websites of the 18th German Bundestag that were crawled to identify the Twitter screennames of German politicians of all Bundestag factions. Because the <em>Alternative für Deutschland (AfD)</em> and the <em>Freie Demokratische Partei (FDP)</em> were not part of the 18th German Bundestag (but it was likely that they will enter the 19th German Bundestag) other official websites were selected to crawl for relevant and representative politicians for these both parties (in case of the <em>AfD</em> this was the website of the directorate of the <em>AfD</em> federal party and the list of members of the European Parliament, in case of the <em>FDP</em> this was the website of the executive committee of the <em>FDP</em> federal party of Germany).</li> <li><strong>followed-accounts.json</strong> contains the (manually checked and edited) crawling result of 327 Twitter screennames of politicians that have been observed via the Twitter streaming API to collect this dataset.</li> </ul>
1.6 million Twitter accounts (german)
<p>data dump of 1.6 million twitter accounts.</p> <p>tsv files have the same number of lines and can be interpreted as columns of a database</p> <p>bin files have the same number of data records, e.g. 32-bit-integer</p> <p>You can find the documentation on https://github.com/MichaelKreil/twitter-analysis</p>
nlp2023_toxic_german
<p>The dataset was sourced from the Austrian newspaper DerStandard (www.derStandard.at) and provided in JSON file format. The dataset encompasses responses to articles published between November 4, 2021, and November 10, 2021 and consisted of 4473 postings belonging to 522 different articles. Only original comments were selected and responses to other comments were excluded. With 2818 nontoxic and 1655 toxic entries the dataset is not very unbalanced. Beside the comments and the binary class label, the date was partly also annotated with four different labels with the position in the text.</p> <p>These labels are</p> <ul> <li>Vulgarity Obscene, foul or boorish language that is inappropriate or improper for civilised discourse, directed or undirected.</li> <li>Target: Individual The target of a toxic comment is an individual</li> <li>Target: Group The target of a toxic comment</li> <li>Target: Other the target of an insult or an incite is not a person or a group of people is a group.</li> </ul>
Figure 8 in Hermann Karsten (1817-1908): a German naturalist in the Neotropics and the significance of his paleovertebrate collection
Figure 8. Mammalia indet. specimens. (a–b) Occipital condyle (MB.Ma. 42899); (c) fragment of sacral vertebra (MB.Ma. 42908); (d–f) indet. fragment of postcranial element (MB.Ma. 33539); (g–i) dorsal rib fragments (MB.Ma. 17145, MB.Ma. 17149); and (j–k) fragments of indet. postcranial elements (MB.Ma. 42909, MB.Ma. 42910, MB.Ma. 42911). Scale bar equals 2 cm.
Figure 7 in Hermann Karsten (1817-1908): a German naturalist in the Neotropics and the significance of his paleovertebrate collection
Figure 7. Notoungulata, proboscidean and mammalian indet. specimens. (a–d) Pisiform bone of Toxodontidae indet. (MB.Ma. 33542). (e–o) Gomphotheriidae indet.: (e–g) thoracic vertebra (MB.Ma. 17152); (h–j) left metacarpal II (MB.Ma. 17146); (k–m) right unciform (MB.Ma. 17147); (n–o) right astragalus (MB.Ma. 17148). (p–s) Mammalia indet.: (p–q) left humeral? head (MB.Ma. 17150); (r–s) femoral head (MB.Ma. 14109). Scale bar equals 2 cm.
Figure 6 in Hermann Karsten (1817-1908): a German naturalist in the Neotropics and the significance of his paleovertebrate collection
Figure 6. "Ground sloth" and glyptodont specimens. (a–k) Tardigrada indet. (a–h) Centra thoracic vertebrae ((a–b) MB.Ma. 17154; (c–d) MB.Ma. 17153; (e–f) MB.Ma. 17156; (g–h) MB.Ma. 17155); (i–k) centra of caudal vertebra (MB.Ma. 33543)], (l–r) Right hemimandible and two upper molariforms of Glyptotherium cf. G. cylindricum (MB.Ma. 33532), and (s–z) Glyptodontidae indet. [(s–t) osteoderm (MB.Ma. 33533-1); (u–z) four carpal–metacarpal elements of indet. laterality (MB.Ma. 33533-2-6). Scale bar equals 2 cm.
Figure 5 in Hermann Karsten (1817-1908): a German naturalist in the Neotropics and the significance of his paleovertebrate collection
Figure 5. "Ground sloth" specimens. (a–n) Tardigrada indet. (a–b) Glenoid fossa of scapula (MB.Ma. 42900); (c–d) left? magnum (MB.Ma. 33540); (e–f) right astragalus (MB.Ma. 42904); (g–h) metatarsal V of indet. laterality (MB.Ma. 42907); (i–j) left metatarsal V (MB.Ma. 42906); (k–l) second phalanx of indet. laterality (MB.Ma. 33537); (m–n) distal fragment of metapodial of indet. laterality (MB.Ma. 33538). Scale bar equals 2 cm.
Figure 4 in Hermann Karsten (1817-1908): a German naturalist in the Neotropics and the significance of his paleovertebrate collection
Figure 4. "Ground sloth" specimens. (a–b) Tooth of Mylodontinae indet. (MB.Ma. 33535) and (c–r) Tardigrada indet. c–d Distal fragment of left femur (MB.Ma. 42896); (e–f) distal fragment of right tibia (MB.Ma. 42897); (g–h) distal fragment of metapodial of indet. position (MB.Ma. 42905); (i–j) proximal fragment of right ulna (MB.Ma. 42902); (k) humeral head of indet. laterality (MB.Ma. 42901); (l–n) fragment of ungual phalanx (MB.Ma. 33541); (o–p) left glenoid fossa of scapula (MB.Ma. 17151); (q–r) proximal phalanx of indet. Position (MB.Ma. 33536). Scale bar equals 3 cm.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.